OpenRaiser/Intern-Atlas huggingface dataset:
Evaluating and Improving the Robustness of LiDAR Odometry and Localization Under Real-World Corruptions
Venue: arXiv cs.RO 2026
Year: 2026

LiDAR odometry and localization are two widely used and fundamental applications in robotic and autonomous driving systems. Although state-of-the-art (SOTA) systems achieve high accuracy on clean point clouds, their robustness to corrupted data remains largely unexplored. We present the first comprehensive benchmark to evaluate the robustness of LiDAR pose-estimation techniques under 18 realistic synthetic corruptions. Our results show that, under these corruptions, odometry position errors escalate from 0.5% to more than 80%, while localization performance stays consistently high. To address this sensitivity, we propose two complementary strategies. First, we design a lightweight detection-and-filter pipeline that classifies the point cloud corruption and applies a corresponding filter (e.g., bilateral filter for noise) to restore the point cloud quality. Our classifier accurately identifies each corruption type, and the filter effectively restores odometry accuracy to near-clean data levels. Second, for learning-based systems, we show that fine-tuning using the corrupted data substantially improves robustness across all tested corruptions and even boosts performance on clean point clouds on one data sequence.
Generation of Uncertainty-Aware High-Level Spatial Concepts in Factorized 3D Scene Graphs via Graph Neural Networks
Venue: arXiv cs.RO 2026
Year: 2026

Enabling robots to autonomously discover high-level spatial concepts (e.g., rooms and walls) from primitive geometric observations (e.g., planar surfaces) within 3D Scene Graphs is essential for robust indoor navigation and mapping. These graphs provide a hierarchical metric-semantic representation in which such concepts are organized. To further enhance graph-SLAM performance, Factorized 3D Scene Graphs incorporate these concepts as optimization factors that constrain relative geometry and enforce global consistency. However, both stages of this process remain largely manual: concepts are typically derived using hand-crafted, concept-specific heuristics, while factors and their covariances are likewise manually designed. This reliance on manual specification limits generalization across diverse environments and scalability to new concept classes. This paper presents a novel learning-based method that infers spatial concepts online from observed vertical planes and introduces them as optimizable factors within a SLAM backend, eliminating the need to handcraft concept generation, factor design, and covariance specification. We evaluate our approach in simulated environments with complex layouts, improving room detection by 20.7% and trajectory estimation by 19.2%, and further validate it on real construction sites, where room detection improves by 5.3% and map matching accuracy by 3.8%. Results confirm that learned factors can improve their handcrafted counterparts in SLAM systems and serve as a foundation for extending this approach to new spatial concepts.
Multi-View Adaptive Contrastive Learning for Information Retrieval Based Fault Localization
Venue: arXiv cs.SE 2026
Year: 2026

Most studies focused on information retrieval-based techniques for fault localization, which built representations for bug reports and source code files and matched their semantic vectors through similarity measurement. However, such approaches often ignore some useful information that might help improve localization performance, such as 1) the interaction relationship between bug reports and source code files; 2) the similarity relationship between bug reports; and 3) the co-citation relationship between source code files. In this paper, we propose a novel approach named Multi-View Adaptive Contrastive Learning for Information Retrieval Fault Localization (MACL-IRFL) to learn the above-mentioned relationships for software fault localization. Specifically, we first generate data augmentations from report-code interaction view, report-report similarity view and code-code co-citation view separately, and adopt graph neural network to aggregate the information of bug reports or source code files from the three views in the embedding process. Moreover, we perform contrastive learning across these views. Our design of contrastive learning task will force the bug report representations to encode information shared by report-report and report-code views,and the source code file representations shared by code-code and report-code views, thereby alleviating the noise from auxiliary information. Finally, to evaluate the performance of our approach, we conduct extensive experiments on five open-source Java projects. The results show that our model can improve over the best baseline up to 28.93%, 25.57% and 20.35% on Accuracy@1, MAP and MRR, respectively.
Exploiting Minority Pseudo-Labels for Semi-Supervised Fine-grained Road Scene Understanding
Venue: arXiv cs.CV 2026
Year: 2026

In fine-grained road scene understanding, semantic segmentation plays a crucial role in enabling vehicles to perceive and comprehend their surroundings. By assigning a specific class label to each pixel in an image, it allows for precise identification and localization of detailed road features, which is vital for high-quality scene understanding and downstream perception tasks. A key challenge in this domain lies in improving the recognition performance of minority classes while mitigating the dominance of majority classes, which is essential for achieving balanced and robust overall performance. However, traditional semi-supervised learning methods often train models overlooking the imbalance between classes. To address this issue, firstly, we propose a general training module that learns from all the pseudo-labels without a conventional filtering strategy. Secondly, we propose a professional training module to learn specifically from reliable minority-class pseudo-labels identified by a novel mismatch score metric. The two modules are crossly supervised by each other so that it reduces model coupling which is essential for semi-supervised learning. During contrastive learning, to avoid the dominance of the majority classes in the feature space, we propose a strategy to assign evenly distributed anchors for different classes in the feature space. Experimental results on multiple public benchmarks show that our method surpasses traditional approaches in recognizing tail classes.
FlowRL: Flow-Augmented Few-Shot Reinforcement Learning for Semi-Structured Sensor Data
Venue: arXiv cs.LG 2026
Year: 2026

Reinforcement learning (RL) in few-shot scenarios with limited sensor data is challenging due to insufficient training samples, particularly in applications like Dynamic Voltage and Frequency Scaling (DVFS) where sensor readings are semi-structured with inherent correlations. We propose Flow-Augmented Reinforcement Learning (FlowRL), a novel method that leverages continuous normalizing flows to generate high-quality synthetic data for few-shot RL. By integrating latent space bootstrapping for diversity and feature-weighted flow matching to preserve critical data correlations, FlowRL enhances sample efficiency and policy robustness. Evaluated on a DVFS case study using the NVIDIA Jetson TX2, our approach achieves up to 35\% higher frame rates and faster Q-value convergence compared to baselines, demonstrating its effectiveness in resource-constrained environments. FlowRL generalizes to other semi-structured domains, such as robotics and smart grids, offering a scalable solution for data-scarce RL settings.
Why Is Anything Conscious?
Venue: arXiv cs.AI 2026
Year: 2026

We tackle the problem of consciousness by taking the naturally selected, embodied organism as our starting point. We provide a formalism describing how biological systems such as human bodies self-organize to hierarchically interpret unlabelled sensory information according to valence. The system is attracted and repelled at different spatial and temporal scales. This is a qualitative interpretation of an unlabelled physical state. We show how such interpretations imply behavioural policies which are differentiated from each other only by this qualitative aspect of information processing. Natural selection favours systems that actively intervene in the world to achieve homeostatic and reproductive goals. Put provocatively, death grounds meaning. This means that in living systems information processing is necessarily subjective, that is, it has quality embedded into its very core. Qualitative information processing involves interoceptive and exteroceptive classifiers, and determines priorities for self-survival. We formulate The Psychophysical Principle of Causality as a theorem, and prove generalisation optimal learning forces this valence first ontology. Qualitative good or bad processing necessarily comes \textit{before} quality neutral representations of properties (i.e. ``red'' is constructed from valence). Under selection pressures like sophisticated predation this produces a hierarchy of selves, of which reafference and reflective self awareness are a consequence. We discuss this in light of the seminal distinction between phenomenal and access consciousness. We claim that phenomenal consciousness without access is likely common, but the reverse is implausible. Our proposal lays the foundation of a formal science of consciousness, closer to human fact than zombie fiction.
HiFi-Glot: High-Fidelity Neural Formant Synthesis with Differentiable Resonant Filters
Venue: arXiv cs.SD 2026
Year: 2026

Formant synthesis aims to generate speech with controllable formant structures, enabling precise control of vocal resonance and phonetic features. However, while existing formant synthesis approaches enable precise formant manipulation, they often yield an impoverished speech signal by failing to capture the complex co-occurring acoustic cues essential for naturalness. To address this issue, this letter presents HiFi-Glot, an end-to-end neural formant synthesis system that achieves both precise formant control and high-fidelity speech synthesis. Specifically, the proposed model adopts a source--filter architecture inspired by classical formant synthesis, where a neural vocoder generates the glottal excitation signal, and differentiable resonant filters model the formants to produce the speech waveform. Experiment results demonstrate that our proposed HiFi-Glot model can generate speech with higher perceptual quality and naturalness while exhibiting a more precise control over formant frequencies, outperforming industry-standard formant manipulation tools such as Praat. Code, checkpoints, and representative audio samples are available at https://www.yichenggu.com/HiFi-Glot/.
InterMind: Doctor-Patient-Family Interactive Depression Assessment Empowered by Large Language Models
Venue: arXiv cs.HC 2026
Year: 2026

Depression poses significant challenges to patients and healthcare organizations, necessitating efficient assessment methods. Existing paradigms typically focus on a patient-doctor way that overlooks multi-role interactions, such as family involvement in the evaluation and caregiving process. Moreover, current automatic depression detection (ADD) methods usually model depression detection as a classification or regression task, lacking interpretability for the decision-making process. To address these issues, we developed InterMind, a doctor-patient-family interactive depression assessment system empowered by large language models (LLMs). Our system enables patients and families to contribute descriptions, generates assistive diagnostic reports for doctors, and provides actionable insights, improving diagnostic precision and efficiency. To enhance LLMs' performance in psychological counseling and diagnostic interpretability, we integrate retrieval-augmented generation (RAG) and chain-of-thoughts (CoT) techniques for data augmentation, which mitigates the hallucination issue of LLMs in specific scenarios after instruction fine-tuning. Quantitative experiments and professional assessments by clinicians validate the effectiveness of our system.
A new baseline for edge detection: Make Encoder-Decoder great again
Venue: arXiv cs.CV 2026
Year: 2026

The performance of deep learning based edge detector has far exceeded that of humans, but the huge computational cost and complex training strategy hinder its further development and application. In this paper, we eliminate these complexities with a vanilla encoder-decoder based detector. Firstly, we design a bilateral encoder to decouple the extraction process of location features and semantic features. Since the location branch no longer provides cues for the semantic branch, the richness of features can be further compressed, which is the key to make our model more compact. We propose a cascaded feature fusion decoder, where the location features are progressively refined by semantic features. The refined location features are the only basis for generating the edge map. The coarse original location features and semantic features are avoided from direct contact with the final result. So the noise in the location features and the location error in the semantic features can be suppressed in the generated edge map. The proposed New Baseline for Edge Detection (NBED) achieves superior performance consistently across multiple edge detection benchmarks, even compared with those methods with huge computational cost and complex training strategy. The ODS of NBED on BSDS500 is 0.838, achieving state-of-the-art performance. Our study shows that what really matters in the current edge detection is high-quality features, and we can make the encoder-decoder based detector great again even without complex training strategies and huge computational cost. The code is available at https://github.com/Li-yachuan/NBED.
A Hierarchical Gradient Tracking Algorithm for Mitigating Subnet-Drift in Fog Learning Networks
Venue: arXiv cs.NI 2026
Year: 2026

Federated learning (FL) encounters scalability challenges when implemented over fog networks that do not follow FL's conventional star topology architecture. Semi-decentralized FL (SD-FL) has proposed a solution for device-to-device (D2D) enabled networks that divides model cooperation into two stages: at the lower stage, D2D communications is employed for local model aggregations within subnetworks (subnets), while the upper stage handles device-server (DS) communications for global model aggregations. However, existing SD-FL schemes are based on gradient diversity assumptions that become performance bottlenecks as data distributions become more heterogeneous. In this work, we develop semi-decentralized gradient tracking (SD-GT), the first SD-FL methodology that removes the need for such assumptions by incorporating tracking terms into device updates for each communication layer. Our analytical characterization of SD-GT reveals upper bounds on convergence for non-convex, convex, and strongly-convex problems. We show how the bounds enable the development of an optimization algorithm that navigates the performance-efficiency trade-off by tuning subnet sampling rate and D2D rounds for each global training interval. Our subsequent numerical evaluations demonstrate that SD-GT obtains substantial improvements in trained model quality and communication cost relative to baselines in SD-FL and gradient tracking on several datasets.
LoRA Provides Differential Privacy by Design via Random Sketching
Venue: arXiv cs.LG 2026
Year: 2026

Low-rank adaptation of language models has been proposed to reduce the computational and memory overhead of fine-tuning pre-trained language models. LoRA incorporates trainable low-rank matrices into some parameters of the pre-trained model, called adapters. In this work, we show theoretically that the low-rank adaptation mechanism of LoRA is equivalent to fine-tuning adapters with noisy batch gradients, with the noise variance being a decreasing function of adaptation rank ($r$). Motivated by this understanding, we prove inherent differential privacy for LoRA when adaptation matrices $A_\ell$ are frozen. We show that various factors, e.g., the adaptation rank and batch size, affect the guaranteed privacy level. Our findings provide useful insights into LoRA and uncovers the reason behind the robustness of models fine-tuned with LoRA to privacy attacks.
A study on the effects of mixed explicit and implicit communications in human-artificial-agent interactions
Venue: arXiv cs.RO 2026
Year: 2026

Communication between humans and artificial agents is essential for their interaction. This is often inspired by human communication, which uses gestures, facial expressions, gaze direction, and other explicit and implicit means. This work presents interaction experiments where humans and artificial agents interact through explicit and implicit communication to evaluate the effect of mixed explicit-implicit communication against purely explicit communication and the impact of the task difficulty in this evaluation. Results obtained using Bayesian parameter estimation show that the task execution time did not significantly change when mixed explicit and implicit communications were used in neither of our experiments, which varied in the type of artificial agent (virtual agent and humanoid robot) used and task difficulty. The number of errors was affected by the communication only when the human was executing a more difficult task, and an impact on the perceived efficiency of the interaction was only observed in the interaction with the robot, for both easy and difficult tasks. In contrast, acceptance, sociability, and transparency of the artificial agent increased when using mixed communication modalities in both our experiments and task difficulty levels. This suggests that task-related measures, such as time, number of errors, and perceived efficiency of the interaction, as well as the impact of the communication on them, are more sensitive to the type of task and the difficulty level, whereas the combination of explicit and implicit communications more consistently improves human perceptions about artificial agents.
Analog fast Fourier transforms for scalable and efficient signal processing
Venue: arXiv cs.ET 2026
Year: 2026

Edge devices are being deployed at increasing volumes to sense and act on information from the physical world. The discrete Fourier transform (DFT) is often necessary to make this sensed data suitable for further processing -- such as by artificial intelligence (AI) algorithms -- and for transmission over communication networks. Analog in-memory computing has been shown to be a fast, energy-efficient, and scalable solution for processing edge AI workloads, but not for Fourier transforms. This is because of the existence of the fast Fourier transform (FFT) algorithm, which enormously reduces the complexity of the DFT but has so far belonged only to digital processors. Here, we show that the FFT can be mapped to analog in-memory computing systems, enabling them to efficiently scale to arbitrarily large Fourier transforms without requiring large sizes or large numbers of non-volatile memory arrays. We experimentally demonstrate analog FFTs on 1D audio and 2D image signals, performing analog computations on up to 524K charge-trapping memory devices simultaneously, where each device has precisely tunable, low-conductance analog states. The scalability of both the new analog FFT approach and the charge-trapping memory device is leveraged to compute a 65,536-point analog DFT, a scale that is otherwise inaccessible by analog systems and which is $>$500$\times$ larger than any previous analog DFT demonstration. Analog FFT cores can provide higher energy efficiency and performance per area than specialized digital FFT processors at all FFT sizes, while also functioning as efficient matrix multiplication engines for AI workloads.
DATransNet: Dynamic Attention Transformer Network for Infrared Small Target Detection
Venue: arXiv cs.CV 2026
Year: 2026

Infrared small target detection (ISTD) is widely used in civilian and military applications. However, ISTD encounters several challenges, including the tendency for small and dim targets to be obscured by complex backgrounds. To address this issue, we propose the Dynamic Attention Transformer Network (DATransNet), which aims to extract and preserve detailed information vital for small targets. DATransNet employs the Dynamic Attention Transformer (DATrans), simulating central difference convolutions (CDC) to extract gradient features. Furthermore, we propose a global feature extraction module (GFEM) that offers a comprehensive perspective to prevent the network from focusing solely on details while neglecting the global information. We compare the network with state-of-the-art (SOTA) approaches and demonstrate that our method performs effectively. Our source code is available at https://github.com/greekinRoma/DATransNet.
Faster algorithms for packing forests in graphs and related problems
Venue: arXiv cs.DS 2026
Year: 2026

We consider several problems related to packing forests in graphs. The first one is to find $k$ edge-disjoint forests in a directed graph $G$ of maximal size such that the indegree of each vertex in these forests is at most $k$. We describe a min-max characterization for this problem and show that it can be solved in almost linear time for fixed $k$, extending the algorithm of [Gabow, 1995]. Specifically, the complexity is $O(k δm \log n)$, where $n, m$ are the number of vertices and edges in $G$ respectively, and $δ= \max\{1, k - k_G\}$, where $k_G$ is the edge connectivity of the graph. Using our solution to this problem, we improve complexities for two existing applications: (1) $k$-forest problem: find $k$ forests in an undirected graph $G$ maximizing the number of edges in their union. We show how to solve this problem in $O(k^3 \min\{kn, m\} \log^2 n + k \cdot{\rm MAXFLOW}(m, m) \log n)$ time, breaking the $O_k(n^{3/2})$ complexity barrier of previously known approaches. (2) Directed edge-connectivity augmentation problem: find a smallest set of directed edges whose addition to the given directed graph makes it strongly $k$-connected. We improve the deterministic complexity for this problem from $O(k δ(m+δn)\log n)$ [Gabow, STOC 1994] to $O(k δm \log n)$. A similar approach with the same complexity also works for the undirected version of the problem.
"Hiding in Plain Sight": Designing Synthetic Dialog Generation for Uncovering Socially Situated Norms
Venue: arXiv cs.CL 2026
Year: 2026

Naturally situated conversations encapsulate the social norms inherent to their context, reflecting both the relationships between interlocutors and the underlying communicative intent. In this paper, we propose a novel, multi-step framework for generating dialogues that automatically uncovers social norms from rich, context-laden interactions through a process of self-assessment and norm discovery, rather than relying on predefined norm labels. Leveraging this framework, we construct NormHint, a comprehensive synthetic dialogue dataset spanning a wide range of interlocutor attributes (e.g., age, profession, personality), relationship types, conversation topics, and conversational trajectories. NormHint is meticulously annotated with turn-level norm violation information, detailed participant descriptions, and remediation suggestions-including alternative trajectories achieved through early intervention. Human validation and automated analysis demonstrate that our dataset captures diverse conversational topics with high naturalness and realism. Moreover, we discovered that fine-tuning a model with our norm violation data significantly enhances its ability to detect and understand potential norm violations in conversations.